Overview
Data exploration using the data prepared by combining the “schools master lists” from the Department of Basic Education website for schools in South Africa.
# LIBRARIES ----------------------------------------------------------------------------------------
library(dplyr)
library(here)
library(ggplot2)
library(plotly)
# READ DATA ----------------------------------------------------------------------------------------
sa_schools <- readRDS(here::here("data/03_sa_schools.RDS"))
# VISUALISE ----------------------------------------------------------------------------------------
p <- ggplot(sa_schools, aes(x = Learners)) +
geom_histogram() +
labs(title='Histogram of no. of learners',
x= 'No. of learners', y = 'School count')
ggplotly(p)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Removed 309 rows containing non-finite values (stat_bin).
p <- ggplot(sa_schools, aes(x = Educators)) +
geom_histogram() +
labs(title='Histogram of no. of educators',
x= 'No. of educators', y = 'School count')
ggplotly(p)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Removed 683 rows containing non-finite values (stat_bin).
ggplotly(p)
Removed 1326 rows containing non-finite values (stat_count).

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